Results 71 to 80 of about 15,175 (257)

Determinants of Body Mass Index in Early Systemic Sclerosis: Implications for Nutritional Risk Stratification

Arthritis Care &Research, EarlyView.
Objective Gastrointestinal (GI) involvement can lead to malnutrition in patients with systemic sclerosis (SSc). Body mass index (BMI) remains the most widely used marker to screen nutritional status. We aimed to identify predictors of lower BMI in patients with SSc. Methods Patients with SSc from a prospective US cohort meeting 2013 American College of
Ali Y. Ayla, Meng Zhang, Bingrui Chen, Claudia Pedroza, Brian Skaug, Maureen Mayes, Michael Hughes, Shervin Assassi, Zsuzsanna H. McMahan   +8 more
wiley   +1 more source

Hamiltonian learning via inverse physics-informed neural networks

Physical Review Research
Hamiltonian learning (HL), enabling precise estimation of system parameters and underlying dynamics, plays a critical role in characterizing quantum systems.
Jie Liu, Xin Wang
doaj   +1 more source

On the stability and convergence of physics informed neural networks

IMA Journal of Numerical Analysis
Abstract Physics Informed Neural Networks is a numerical method that uses neural networks to approximate solutions of partial differential equations. It has received a lot of attention and is currently used in numerous physical and engineering problems.
Dimitrios Gazoulis, Ioannis Gkanis, Charalambos G. Makridakis   +2 more
openaire   +2 more sources

Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane, Salama Makni, Mourad Kchaou, Ahmed El Hajjaji   +3 more
wiley   +1 more source

Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

IEEE Open Journal of Antennas and Propagation, 2020
In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics. The key component of our model is a recurrent neural network, which learns representations of long-term spatial ...
Oameed Noakoasteen, Shu Wang, Zhen Peng, Christos Christodoulou   +3 more
doaj   +1 more source

Light‐Assisted 3D Printing Techniques and Photocrosslinking Strategies: Recent Advances in Musculoskeletal Tissue Engineering

Advanced Engineering Materials, EarlyView.
In this review, the current state of light‐assisted 3D printing as it pertains to engineering musculoskeletal tissues including bone, cartilage, skeletal muscle, tendon, and ligaments is summarized. Common printing techniques, photoreactive materials, and study design choices are compiled and reviewed.
Meagan Morgan, Bin Zhang, Roger Narayan
wiley   +1 more source

Physics-informed neural networks for granular flows modelling

Mechanics & Industry
Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful framework for solving forward and inverse problems involving partial differential equations, by embedding physical laws directly into the training process of neural networks.
Baldoni Barbara, Delcey Mickaël, Cheny Yoann, Gans Adrien, Jenny Mathieu, Kiesgen de Richter Sébastien   +5 more
doaj   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Neural Network Models for Predicting Magnetization Surface Switched Reluctance Motor: Classical, Radial Basis Function, and Physics-Informed Techniques

IEEE Access
Neural networks have increasingly been utilized in electric drive systems to enhance modeling, control, and optimization. These data-driven techniques enable accurate predictions of complex nonlinear behaviors, including the magnetization characteristics
Galina Demidova, Jackson J. Justo, Dmitry Lukichev, Nikolai Poliakov, Alecksey Anuchin   +4 more
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source